@inproceedings{kim2013context-dependent,
author = {Kim, Dongwoo and Wang, Haixun and Oh, Alice},
title = {Context-Dependent Conceptualization},
booktitle = {IJCAI},
year = {2013},
month = {January},
abstract = {Conceptualization seeks to map a short text to a set of concepts as a mechanism of understanding text. Most of prior research in conceptualization uses human-crafted knowledge bases that map instances to concepts. Such approaches to conceptualization have the limitation that the mappings are not context sensitive. To overcome this limitation, we propose a framework in which we harness the power of a probabilistic topic model which inherently captures the semantic relations between words. By combining latent Dirichlet allocation, a widely used topic model with Probase, a large-scale probabilistic knowledge base, we develop a corpus-based framework for context-dependent conceptualization. Through this simple but powerful framework, we improve conceptualization and enable a wide range of applications that rely on semantic understanding of short texts, including frame element prediction, word similarity in context, ad-query similarity, and query similarity.},
url = {http://approjects.co.za/?big=en-us/research/publication/context-dependent-conceptualization/},
edition = {IJCAI},
}